The present invention relates to a method of and an apparatus for supporting data classifying, a program and a recording medium recording the program, and more particularly, it relates to a method of and an apparatus for supporting data classifying for arranging unknown data on any cell with a classification map consisting of an aggregate of a plurality of cells, a program and a recording medium recording the program.
A data classification supporting method for arranging unknown data on any cell with a classification map consisting of an aggregate of a plurality of cells is known in general. A self-organizing map (SOM) proposed by T. Kohonen, for example, is known as a classification map employed for this data classification supporting method. The data classification supporting method with the self-organizing map (SOM) treats unknown data as a multi-dimensional vector for classifying the unknown data to belong to a cell having high similarity with a self-organizing algorithm and displaying the same on a two-dimensional self-organizing map (refer to Japanese Patent Laying-Open No. 2003-44828, for example).
The above Japanese Patent Laying-Open No. 2003-44828 discloses a method of deciding a display position for unknown data of a multi-dimensional vector on a position deviating from a neuron (cell) to which the unknown data belongs on the basis of the distance between the neuron (cell) to which the unknown data belongs or a neuron (cell) in the vicinity thereof and the unknown data in a multi-dimensional vector space when displaying the unknown data on a two-dimensional self-organizing map plane with a self-organizing map. Thus, each unknown data can be identifiably displayed according to the method disclosed in Japanese Patent Laying-Open No. 2003-44828, also when the self-organizing map has only a small number of neurons (cells).
According to the method disclosed in Japanese Patent Laying-Open No. 2003-44828, however, it may be difficult to discriminate a neuron (cell) to which unknown data belongs and a neuron (cell) similar to the unknown data from each other since this method does not display which neuron (cell) is the neuron (cell) to which the unknown data belongs and which neuron (cell) is the neuron (cell) similar to the unknown data.